In paper, chemical, petroleum, power, or other process plants, skilled technicians are involved in performing routine diagnostic and maintenance operations. These operations may involve predicting/determining state of subsystems or components thereof. In order to perform such operations, a technician would typically fetch data required for performing the operation from the plant into a local storage (e.g. a remote client). Thereafter, based on the characteristics of the data imported and nature of diagnosis/maintenance, the technician would select, train and apply a model on the data. According to the results, the technician may perform further diagnosis or take action to prevent or control a process(es) to ensure healthy operation of the process plant.
The above approach is restrictive due to dependence on the technician. The efficiency of the method depends largely on the ability of the technician. Owing to the complex nature of data in process plants, there may be a significantly large number of models (or model ensembles) available for analysis of data. Each model or model ensemble(s), may not be readily applicable on a data set to obtain results. Applying such a model or model ensemble(s) may require expertise on various learning and analysis models, and experience of working with a wide variety of model. If the technician fails to accurately select the model and train the model, it is likely that the results of the analysis would be faulty. Further restrictions arise due to the scale/distribution of data generated in a process plant. Accordingly, migrating data within or outside the process plant is a challenge and has disadvantages.
In view of the above, there is a need to have an improved method and system for controlling operations within a process plant.